246 lines
8.4 KiB
Python
246 lines
8.4 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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'''
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Modified from https://github.com/facebookresearch/ConvNeXt
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Copyright (c) Meta Platforms, Inc. and affiliates.
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All rights reserved.
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This source code is licensed under the license found in the
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LICENSE file in the root directory of this source tree.
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'''
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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from paddle.nn.initializer import Constant
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import numpy as np
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from ppdet.core.workspace import register, serializable
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from ..shape_spec import ShapeSpec
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from .transformer_utils import DropPath, trunc_normal_, zeros_
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__all__ = ['ConvNeXt']
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class Block(nn.Layer):
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r""" ConvNeXt Block. There are two equivalent implementations:
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(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
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(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
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We use (2) as we find it slightly faster in Pypaddle
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Args:
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dim (int): Number of input channels.
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drop_path (float): Stochastic depth rate. Default: 0.0
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layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
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"""
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def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
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super().__init__()
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self.dwconv = nn.Conv2D(
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dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
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self.norm = LayerNorm(dim, eps=1e-6)
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self.pwconv1 = nn.Linear(
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dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
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self.act = nn.GELU()
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self.pwconv2 = nn.Linear(4 * dim, dim)
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if layer_scale_init_value > 0:
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self.gamma = self.create_parameter(
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shape=(dim, ),
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attr=ParamAttr(initializer=Constant(layer_scale_init_value)))
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else:
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self.gamma = None
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity(
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)
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def forward(self, x):
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input = x
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x = self.dwconv(x)
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x = x.transpose([0, 2, 3, 1])
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x = self.norm(x)
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x = self.pwconv1(x)
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x = self.act(x)
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x = self.pwconv2(x)
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if self.gamma is not None:
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x = self.gamma * x
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x = x.transpose([0, 3, 1, 2])
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x = input + self.drop_path(x)
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return x
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class LayerNorm(nn.Layer):
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r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
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The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
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shape (batch_size, height, width, channels) while channels_first corresponds to inputs
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with shape (batch_size, channels, height, width).
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"""
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def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
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super().__init__()
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self.weight = self.create_parameter(
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shape=(normalized_shape, ),
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attr=ParamAttr(initializer=Constant(1.)))
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self.bias = self.create_parameter(
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shape=(normalized_shape, ),
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attr=ParamAttr(initializer=Constant(0.)))
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self.eps = eps
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self.data_format = data_format
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if self.data_format not in ["channels_last", "channels_first"]:
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raise NotImplementedError
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self.normalized_shape = (normalized_shape, )
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def forward(self, x):
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if self.data_format == "channels_last":
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return F.layer_norm(x, self.normalized_shape, self.weight,
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self.bias, self.eps)
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elif self.data_format == "channels_first":
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u = x.mean(1, keepdim=True)
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s = (x - u).pow(2).mean(1, keepdim=True)
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x = (x - u) / paddle.sqrt(s + self.eps)
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x = self.weight[:, None, None] * x + self.bias[:, None, None]
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return x
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@register
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@serializable
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class ConvNeXt(nn.Layer):
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r""" ConvNeXt
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A Pypaddle impl of : `A ConvNet for the 2020s` -
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https://arxiv.org/pdf/2201.03545.pdf
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Args:
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in_chans (int): Number of input image channels. Default: 3
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depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
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dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
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drop_path_rate (float): Stochastic depth rate. Default: 0.
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layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
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"""
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arch_settings = {
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'tiny': {
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'depths': [3, 3, 9, 3],
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'dims': [96, 192, 384, 768]
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},
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'small': {
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'depths': [3, 3, 27, 3],
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'dims': [96, 192, 384, 768]
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},
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'base': {
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'depths': [3, 3, 27, 3],
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'dims': [128, 256, 512, 1024]
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},
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'large': {
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'depths': [3, 3, 27, 3],
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'dims': [192, 384, 768, 1536]
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},
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'xlarge': {
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'depths': [3, 3, 27, 3],
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'dims': [256, 512, 1024, 2048]
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},
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}
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def __init__(
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self,
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arch='tiny',
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in_chans=3,
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drop_path_rate=0.,
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layer_scale_init_value=1e-6,
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return_idx=[1, 2, 3],
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norm_output=True,
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pretrained=None, ):
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super().__init__()
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depths = self.arch_settings[arch]['depths']
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dims = self.arch_settings[arch]['dims']
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self.downsample_layers = nn.LayerList(
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) # stem and 3 intermediate downsampling conv layers
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stem = nn.Sequential(
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nn.Conv2D(
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in_chans, dims[0], kernel_size=4, stride=4),
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LayerNorm(
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dims[0], eps=1e-6, data_format="channels_first"))
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self.downsample_layers.append(stem)
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for i in range(3):
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downsample_layer = nn.Sequential(
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LayerNorm(
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dims[i], eps=1e-6, data_format="channels_first"),
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nn.Conv2D(
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dims[i], dims[i + 1], kernel_size=2, stride=2), )
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self.downsample_layers.append(downsample_layer)
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self.stages = nn.LayerList(
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) # 4 feature resolution stages, each consisting of multiple residual blocks
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dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))]
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cur = 0
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for i in range(4):
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stage = nn.Sequential(* [
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Block(
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dim=dims[i],
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drop_path=dp_rates[cur + j],
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layer_scale_init_value=layer_scale_init_value)
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for j in range(depths[i])
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])
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self.stages.append(stage)
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cur += depths[i]
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self.return_idx = return_idx
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self.dims = [dims[i] for i in return_idx] # [::-1]
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self.norm_output = norm_output
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if norm_output:
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self.norms = nn.LayerList([
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LayerNorm(
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c, eps=1e-6, data_format="channels_first")
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for c in self.dims
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])
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self.apply(self._init_weights)
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if pretrained is not None:
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if 'http' in pretrained: #URL
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path = paddle.utils.download.get_weights_path_from_url(
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pretrained)
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else: #model in local path
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path = pretrained
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self.set_state_dict(paddle.load(path))
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def _init_weights(self, m):
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if isinstance(m, (nn.Conv2D, nn.Linear)):
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trunc_normal_(m.weight)
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zeros_(m.bias)
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def forward_features(self, x):
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output = []
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for i in range(4):
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x = self.downsample_layers[i](x)
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x = self.stages[i](x)
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output.append(x)
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outputs = [output[i] for i in self.return_idx]
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if self.norm_output:
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outputs = [self.norms[i](out) for i, out in enumerate(outputs)]
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return outputs
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def forward(self, x):
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x = self.forward_features(x['image'])
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return x
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@property
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def out_shape(self):
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return [ShapeSpec(channels=c) for c in self.dims]
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